Having regard to the large number of vehicles travelling on the roads, it is necessary to procure, for these vehicles and their drivers, the best possible adapted road vision in order to reduce risks of accidents. In particular at night, it is important for the driver to be able to have sufficiently detailed vision of the road extending in front of him as well as the sides of this road. In other words, for questions of safety, it is sought to improve the night vision of the road scene for the driver of the vehicle.
For this, there exist night vision systems in which a lighting device, of the spotlight type, emits an infrared light beam in the direction of the road, in front of the vehicle. This infrared light is reflected by the various objects situated in the road scene. This reflection of the infrared light is more or less intense according to the nature of the object and its distance with respect to the lighting device. A sensor sensitive to infrared radiation, situated generally in the vehicle, provides capture of this infrared radiation. It then supplies an infrared image of the road scene extending in front of the vehicle. Such a system, with the emission of infrared radiation, reflection of these rays and capture of the reflected rays, is referred to as an “active system”. It makes it possible to detect the near infrared, that is to say the radiation having a wavelength which may attain 1100 nm. An example of an image obtained with an active system is shown in FIG. 1. This image makes it possible to detect a vehicle with a pedestrian on the road alongside the vehicle. However, it is not possible to determine whether the lights of this vehicle are the front lights or the brake lights of the vehicle. It is therefore not possible to know in which direction the vehicle is placed. This image also makes it possible to see light spots on the right of the road; these light spots seem to be road signs, but it is impossible to read the information written on these panels.
There also exist systems for detecting far infrared. These systems are called “passive systems”. In these systems, a sensor captures the far infrared light, that is to say radiation having a wavelength of around 10 μm. Such systems make it possible to capture only the infrared radiation emitted by the objects themselves. In other words, it is a case of measuring the temperature of the elements in the road scene. In such a passive system, the sensor captures the head detected, as an infrared light. One example of an image obtained by a passive system is shown in FIG. 2. This image makes it possible to display a first vehicle and, further away, a second vehicle with pedestrians close by. However, it is not possible to determine, on this image, whether the lights of these vehicles are the front lights or the brake lights. It is therefore not possible to know in which direction these vehicles are placed.
All these systems have drawbacks. In particular, the passive systems cannot detect cold objects. This drawback is aggravated further when moving objects, sharing the same space as the vehicle, are invisible. This is the case in particular with cars which are still cold, which have been travelling only for a few moments and where the glasses on the rear lights have not had time to heat up. This is because the large quantity of far infrared radiation emitted by the lamps of the rear lights pass through neither plastic nor glass. Likewise, the illumination of the brake lights, the direction indicators or the hazard warning lights do not instantaneously heat up the glass of the said light. They are therefore undetectable by a passive system.
On the other hand, active systems react too well to light sources such as the rear lights of vehicles, three-coloured lights on the road, etc. These lights, emitting infrared radiation, dazzle the sensor and create a kind of halo of light all around the image of the object in question, which makes the contour of the object undefined. This dazzle is referred to as “blooming”.
Moreover, with these active or passive systems, the road scene is seen at wavelengths which are outside the visible spectrum and therefore by nature foreign to the concept of colour. The image of the road scene obtained by these systems is therefore monochrome (that is to say black and white) with various levels of grey, the light levels corresponding to the objects emitting or reflecting infrared and the dark levels corresponding to the objects not emitting or reflecting infrared. However, with a monochrome image, it is sometimes difficult to know precisely what type of object is concerned. For example, on the images in FIGS. 1 and 2, it is not possible to detect whether it is a case of front or rear lights of the vehicles. Likewise, it is not possible to read the information written on the road signs.
Active or passive systems attempt to remedy these drawbacks by processing the captured image before displaying it. One of these processings consists of a video reversal of the image. This video reversal makes the objects detected as dark light and makes the objects detected as bright dark. An example of an image processed by video reversal is shown in FIG. 3. In this example, the video reversal makes it possible to display the road scene better and to better imagine to what each object in the road scene corresponds. In this example, the video reversal makes it possible to show that the first vehicle is coming in the opposite direction and that the second vehicle is stationary in the same direction as the vehicle in which the system is mounted.
Another processing of the image captured proposes to artificially colour the image of the road scene. This treatment consists of associating with each level of grey of the image captured, an artificial and arbitrary colour. This operation is known, in image processing, by the name “application of an LUT (look-up table)”. The image obtained is called a “false-colour image” since the colours visible on the image are artificial colours which do not correspond to the real colours. For example, the colour red can be associated with a high level of grey and the colour blue with a very low level of grey. The intermediate levels of grey are associated with colours graduated between red and blue. It will thus be understood that, for example, a light situated facing the sensor will have a necessarily red image (high level of grey). It will not therefore be possible to know whether it is a case of a dipped headlight of a vehicle or a brake light. It is therefore not possible to exactly interpret the objects situated in the road scene in front of the vehicle. In other words, these colouring operations may make it possible to improve the perception of an image by revealing information which a simple monochrome display does not make it possible to identify. They do nevertheless remain artifices and in no way render the true colour of the objects. For example, in the case of infrared night vision, objects with the same visible colour (for example green) may have radically opposed behaviours in infrared. One may appear bright or light because, apart from the wavelengths giving it its green colour, the object reflects near infrared (active system) or, because of its temperature, emits far infrared (passive system). The other may appear dark because it absorbs the near infrared and, because of its low temperature, does not emit far infrared.